SoroushVahidi/scidocs huggingface dataset:
Understanding what concerns consumers: a semantic approach to product feature extraction from consumer reviews
(2010)

The Web has become an excellent source for gathering consumer opinions (more specifically, consumer reviews) about products. Consumer reviews are essential for retailers and product manufacturers to understand the general responses of customers to their products and improve their marketing campaigns or products accordingly. In addition, consumer reviews enable retailers to recognize the specific preferences of each customer, which facilitates effective marketing decisions. As the number of consumer reviews expands, it is essential and desirable to develop an efficient and effective sentiment analysis technique that is capable of extracting product features stated in consumer reviews (i.e., product feature extraction) and determining the sentiments (positive or negative semantic orientations) of consumers for these product features (i.e., opinion orientation identification). Product feature extraction is critical to sentiment analysis, because its effectiveness significantly affects the performance of opinion orientation identification, as well as the ultimate effectiveness of sentiment analysis. Therefore, this study concentrates on product feature extraction from consumer reviews. C.-P. Wei (&) Institute of Service Science, College of Technology Management, National Tsing Hua University, Hsinchu, Taiwan, ROC e-mail: cpwei@mx.nthu.edu.tw Y.-M. Chen Department of Information Management, College of Management, National Sun Yat-Sen University, Kaohsiung, Taiwan, ROC C.-S. Yang Department of Information Management, College of Informatics, Yuan-Ze University, Chung-Li, Taiwan, ROC e-mail: csyang@saturn.yzu.edu.tw C. C. Yang College of Information Science and Technology, Drexel University, Philadelphia, PA, USA e-mail: chris.yang@ischool.drexel.edu 123 Inf Syst E-Bus Manage (2010) 8:149–167 DOI 10.1007/s10257-009-0113-9
A random graph model of kidney exchanges: efficiency, individual-rationality and incentives
(2011)

In kidney exchanges, hospitals share patient lists and receive transplantations. A kidney-paired donation (KPD) mechanism needs to promote full sharing of information about donor-patient pairs, and identify a Pareto efficient outcome that also satisfies participation constraints of hospitals. We introduce a random graph model of the KPD exchange and then fully characterize the structure of the efficient outcome and the expected number of transplantations that can be performed. Random graph theory allows early experimental results to be explained analytically, and enables the study of participation incentives in a methodological way. We derive a square-root law between the welfare gains from sharing patient-donor pairs in a central pool and the individual sizes of hospitals, illustrating the urgent need for the nationwide expansion of such programs. Finally, we establish through theoretical and computational analysis that enforcing simple individual rationality constraints on the outcome can mitigate the negative impact of strategic behavior by hospitals.
A continuous-time sigma-delta modulator with 88-dB dynamic range and 1.1-MHz signal bandwidth
(2004)

This paper presents the design and experimental results of a continuous-time /spl Sigma//spl Delta/ modulator for ADSL applications. Multibit nonreturn-to-zero (NRZ) DAC pulse shaping is used to reduce clock jitter sensitivity. The nonzero excess loop delay problem in conventional continuous-time /spl Sigma//spl Delta/ modulators is solved by our proposed architecture. A prototype third-order continuous-time /spl Sigma//spl Delta/ modulator with 5-bit internal quantization was realized in a 0.5-/spl mu/m double-poly triple-metal CMOS technology, with a chip area of 2.4 /spl times/ 2.4 mm/sup 2/. Experimental results show that the modulator achieves 88-dB dynamic range, 84-dB SNR, and 83-dB SNDR over a 1.1-MHz signal bandwidth with an oversampling ratio of 16, while dissipating 62 mW from a 3.3-V supply.
Code coverage for suite evaluation by developers
(2014)

One of the key challenges of developers testing code is determining a test suite's quality -- its ability to find faults. The most common approach is to use code coverage as a measure for test suite quality, and diminishing returns in coverage or high absolute coverage as a stopping rule. In testing research, suite quality is often evaluated by a suite's ability to kill mutants (artificially seeded potential faults). Determining which criteria best predict mutation kills is critical to practical estimation of test suite quality. Previous work has only used small sets of programs, and usually compares multiple suites for a single program. Practitioners, however, seldom compare suites --- they evaluate one suite. Using suites (both manual and automatically generated) from a large set of real-world open-source projects shows that evaluation results differ from those for suite-comparison: statement (not block, branch, or path) coverage predicts mutation kills best.
MiMaze, a 3D Multi-Player Game on the Internet
(1998)

This paper describes the design, implementation, and evaluation of MiMaze, a distributed 3D multiplayer game on the Internet. The major contribution of this work is to provide the first experimental results for this new type of application. An original fully distributed communication architecture has been designed for MiMaze. It uses multicast communication system based on RTP/UDP/IP, and a distributed synchronization mechanisms to guarantee the consistency of the game, regardless of heterogeneous network delay. This paper provides evaluation results on the Mbone. The experimental evaluation shows that the major problem will not be with real-time interaction, but with scalability.
Scalable Distributed Monte-Carlo Tree Search
(2011)

Monte-Carlo Tree Search (MCTS) is remarkably successful in two-player games, but parallelizing MCTS has been notoriously difficult to scale well, especially in distributed environments. For a distributed parallel search, transposition-table driven scheduling (TDS) is known to be efficient in several domains. We present a massively parallel MCTS algorithm, that applies the TDS parallelism to the Upper Confidence bound Applied to Trees (UCT) algorithm, which is the most representative MCTS algorithm. To drastically decrease communication overhead, we introduce a reformulation of UCT called Depth-First UCT. The parallel performance of the algorithm is evaluated on clusters using up to 1,200 cores in artificial game-trees. We show that this approach scales well, achieving 740-fold speedups in the best case.
Diverse and Controllable Image Captioning with Part-of-Speech Guidance
(2018)

Automatically describing an image is an important capability for virtual assistants. Significant progress has been achieved in recent years on this task of image captioning. However, classical prediction techniques based on maximum likelihood trained LSTM nets don’t embrace the inherent ambiguity of image captioning. To address this concern, recent variational auto-encoder and generative adversarial network based methods produce a set of captions by sampling from an abstract latent space. But, this latent space has limited interpretability and therefore, a control mechanism for captioning remains an open problem. This paper proposes a captioning technique conditioned on part-of-speech. Our method provides human interpretable control in form of part-of-speech. Importantly, part-of-speech is a language prior, and conditioning on it provides: (i) more diversity as evaluated by counting n-grams and the novel sentences generated, (ii) achieves high accuracy for the diverse captions on standard captioning metrics.
Credit Card Fraud Detection: A Novel Approach Using Aggregation Strategy and Feedback Mechanism
(2018)

With the rapid development of electronic commerce, the number of transactions by credit cards are increasing rapidly. As online shopping becomes the most popular transaction mode, cases of transaction fraud are also increasing. In this paper, we propose a novel fraud detection method that composes of four stages. To enrich a cardholder’s behavioral patterns, we first utilize the cardholders’ historical transaction data to divide all cardholders into different groups such that the transaction behaviors of the members in the same group are similar. We thus propose a window-sliding strategy to aggregate the transactions in each group. Next, we extract a collection of specific behavioral patterns for each cardholder based on the aggregated transactions and the cardholder’s historical transactions. Then we train a set of classifiers for each group on the base of all behavioral patterns. Finally, we use the classifier set to detect fraud online and if a new transaction is fraudulent, a feedback mechanism is taken in the detection process in order to solve the problem of concept drift. The results of our experiments show that our approach is better than others.
Dynamic Memory Networks for Visual and Textual Question Answering
(2016)

Neural network architectures with memory and attention mechanisms exhibit certain reasoning capabilities required for question answering. One such architecture, the dynamic memory network (DMN), obtained high accuracy on a variety of language tasks. However, it was not shown whether the architecture achieves strong results for question answering when supporting facts are not marked during training or whether it could be applied to other modalities such as images. Based on an analysis of the DMN, we propose several improvements to its memory and input modules. Together with these changes we introduce a novel input module for images in order to be able to answer visual questions. Our new DMN+ model improves the state of the art on both the Visual Question Answering dataset and the bAbI-10k text question-answering dataset without supporting fact supervision.
3D human face description: landmarks measures and geometrical features
(2012)

Morphometric measures and geometrical features are widely used to descript faces. Generally, they are extracted punctually from landmarks, namely anthropometric reference points. The aims are various, such as face recognition, facial expression recognition, face detection, study of changes in facial morphology due to growth, or dysmorphologies. Most of the time, landmarks were extracted with the help of an algorithm or manually located on the faces. Then, measures are computed or geometrical features are extracted to perform the scope of the study. This paper is intended as a survey collecting and explaining all these features, in order to provide a structured user database of the potential parameters and their charateristics. Firstly, facial soft-tissue landmarks are defined and contextualized; then the various morphometric measures are introduced and some results are given; lastly, the most important measures are compared to identify the best one for face recognition applications.
Supporting public health priorities: recommendations for physical education and physical activity promotion in schools.
(2015)

Physical activity (PA) provides numerous physiological and psychosocial benefits. However, lifestyle changes, including reduced PA opportunities in multiple settings, have resulted in an escalation of overweight and obesity and related health problems. Poor physical and mental health, including metabolic and cardiovascular problems is seen in progressively younger ages, and the systematic decline in school PA has contributed to this trend. Of note, the crowded school curriculum with an intense focus on academic achievement, lack of school leadership support, funding and resources, plus poor quality teaching are barriers to PA promotion in schools. The school setting and physical educators in particular, must embrace their role in public health by adopting a comprehensive school PA program. We provide an overview of key issues and challenges in the area plus best bets and recommendations for physical education and PA promotion in the school system moving forward.
Integrating movement in academic classrooms: understanding, applying and advancing the knowledge base.
(2015)

In the context of comprehensive and coordinated approaches to school health, academic classrooms have gained attention as a promising setting for increasing physical activity and reducing sedentary time among children. The aims of this paper are to review the rationale and knowledge base related to movement integration in academic classrooms, consider the practical applications of current knowledge to interventions and teacher education, and suggest directions for future research. Specifically, this paper (i) situates movement integration amid policy and research related to children's health and the school as a health-promoting environment; (ii) highlights the benefits of movement integration; (iii) summarizes movement integration programs and interventions; (iv) examines factors associated with classroom teachers' movement integration; (v) offers strategies for translating research to practice and (vi) forwards recommendations for future inquiry related to the effectiveness and sustainability of efforts to integrate movement into classroom routines. This paper provides a comprehensive resource for developing state-of-the-art initiatives to maximize children's movement in academic classrooms as a key strategy for important goals in both education and public health.
The “ What ” and “ Why ” of Goal Pursuits : Human Needs and the Self-Determination of Behavior
(2000)

Self-determination theory (SDT) maintains that an understanding of human motivation requires a consideration of innate psychological needs for competence, autonomy, and relatedness. We discuss the SDT concept of needs as it relates to previous need theories, emphasizing that needs specify the necessary conditions for psychological growth, integrity, and well-being. This concept of needs leads to the hypotheses that different regulatory processes underlying goal pursuits are differentially associated with effective functioning and well-being and also that different goal contents have different relations to the quality of behavior and mental health, specifically because different regulatory processes and different goal contents are associated with differing degrees of need satisfaction. Social contexts and individual differences that support satisfaction of the basic needs facilitate natural growth processes including intrinsically motivated behavior and integration of extrinsic motivations, whereas those that forestall autonomy, competence, or relatedness are associated with poorer motivation, performance, and well-being. We also discuss the relation of the psychological needs to cultural values, evolutionary processes, and other contemporary motivation theories.
Barriers to Providing Physical Education and Physical Activity in Victorian State Secondary Schools
(2010)

An on-line questionnaire was completed by 115 physical education teachers to establish the barriers to their implementation of physical education in Victorian state secondary schools. In addition, the barriers perceived by teachers to impact on students’ participation in school-based physical education and physical activity were examined. The barriers to the provision of physical education were found to be largely institutional, although two-thirds of respondents recognised their own difficulties in engaging students when teaching as potential obstacles to student participation. Students were also perceived to be influenced by their own (45 per cent) and their peers (62 per cent) low levels of interest when choosing to participate. An awareness of these barriers has implications for physical education teaching, curriculum design, teacher training and adolescent participation in the school environment.
Self-efficacy: toward a unifying theory of behavioral change.
(1977)

The present article presents an integrative theoretical framework to explain and to predict psychological changes achieved by different modes of treatment. This theory states that psychological procedures, whatever their form, alter the level and strength of self-efficacy. It is hypothesized that expectations of personal efficacy determine whether coping behavior will be initiated, how much effort will be expended, and how long it will be sustained in the face of obstacles and aversive experiences. Persistence in activities that are subjectively threatening but in fact relatively safe produces, through experiences of mastery, further enhancement of self-efficacy and corresponding reductions in defensive behavior. In the proposed model, expectations of personal efficacy are derived from four principal sources of information: performance accomplishments, vicarious experience, verbal persuasion, and physiological states. The more dependable the experiential sources, the greater are the changes in perceived selfefficacy. A number of factors are identified as influencing the cognitive processing of efficacy information arising from enactive, vicarious, exhortative, and emotive sources. The differential power of diverse therapeutic procedures is analyzed in terms of the postulated cognitive mechanism of operation. Findings are reported from microanalyses of enactive, vicarious, and emotive modes of treatment that support the hypothesized relationship between perceived self-efficacy and behavioral changes. Possible directions for further research are discussed.
Web-Based Ontology Languages and its Based Description Logics
(2011)

The main object of semantic web languages is to add semantics to the existing information on the Web. These web languages have been developed to represent or express ontologies. Therefore, these description languages provide richer constructors for forming complex class expressions and axioms. An ontology is expressed in a knowledge representation language, which provides a formal frame of semantics. Since description logic is the basis of most ontology language, it is appropriate to explain briefly the base of the description to understand the ontology language clearly. The aim of this paper is to provide a brief survey of state-of-the-art ontology languages which are used to express ontology over the Web is provided. Also the gaol of this paper is to provide a basic understanding of ontologies and description logics, which are the basis of ontology languages.
Smurf-based Distributed Denial of Service (DDoS) Attack Amplification in Internet
(2007)

The Smurf-based distributed denial of service (DDoS) attack is an amplification attack where the attacker uses unprotected intermediate networks to amplify the attack traffic load and direct it to the victim computer. In this paper, we investigate the factors that contribute to the amplification of the smurf attack traffic and understand the relation among the original attack traffic, intermediate unprotected network and the final amplified attack traffic. We also define a new term called attack amplification factor which represents the degree of amplification that original attack traffic undergoes during its transmission towards the victim computer. It is also shown in this paper that utilizing this attack, it is possible for an attacker to just use a dialup modem and an unprotected intermediary network to exhaust even an ultra high speed optical line such as OC-192 of the victim network.
An Attention-based Regression Model for Grounding Textual Phrases in Images
(2017)

Grounding, or localizing, a textual phrase in an image is a challenging problem that is integral to visual language understanding. Previous approaches to this task typically make use of candidate region proposals, where end performance depends on that of the region proposal method and additional computational costs are incurred. In this paper, we treat grounding as a regression problem and propose a method to directly identify the region referred to by a textual phrase, eliminating the need for external candidate region prediction. Our approach uses deep neural networks to combine image and text representations and refines the target region with attention models over both image subregions and words in the textual phrase. Despite the challenging nature of this task and sparsity of available data, in evaluation on the ReferIt dataset, our proposed method achieves a new state-of-the-art in performance of 37.26% accuracy, surpassing the previously reported best by over 5 percentage points. We find that combining image and text attention models and an image attention area-sensitive loss function contribute to substantial improvements.
Intelligent book positioning for library using RFID and book spine matching
(2011)

In library, the management of books is very complication and timing costing. The location of books could be altered by librarian, students, teachers and any one around the library. Therefore, allocating a book is not an easy task in big library. Indoor positioning is an important technology to help storage management and customer services providing. RFID provides a good wireless platform to facilitate indoor positioning. However, duo to the small width of each book spine, adopting positioning based RFID alone is not enough to locate books in a library. In this work, we combine image matching with L-GEM based RBFNN to enhance the accuracy and robustness of the book locating system. We apply this new method in a library environment to position the certain books. Experimental results show that the proposed method is highly accurate and robust to white noise of RFID signals.
An expanding reference library for Peer-to-Peer content
(2011)

Many Peer-to-Peer (P2P) networks have emerged as scalable platforms for enabling file-sharing and content-distribution applications. Due to their reliance on decentralized models encompassing millions of users, P2P networks are subjected to being infested with contraband material, copyright infringing content, as well as malicious viruses and programs. Moreover, the dynamics involved as users join and leave these networks make investigating them quite complex and time-dependent. To that end, this paper presents PxP, a tool that creates an expanding reference library for content that appears on P2P networks. PxP continuously searches the P2P networks for new files, downloads and fingerprints them, deletes them, and add their meta data to a public reference library. We envision that this library will aid investigators in criminal investigations that involve contraband material, civil investigations with copyright infringement content. In addition, this library can be used to speed up forensics examination of media through skipping over some of the files shared on P2P networks and in validating carving tools. We assess the performance of our framework through analysis, simulations and real experiments for content collected from the Gnutella P2P network.
An efficient sparse metric learning in high-dimensional space via l1-penalized log-determinant regularization
(2009)

This paper proposes an efficient sparse metric learning algorithm in high dimensional space via an l1-penalized log-determinant regularization. Compare to the most existing distance metric learning algorithms, the proposed algorithm exploits the sparsity nature underlying the intrinsic high dimensional feature space. This sparsity prior of learning distance metric serves to regularize the complexity of the distance model especially in the "less example number p and high dimension d" setting. Theoretically, by analogy to the covariance estimation problem, we find the proposed distance learning algorithm has a consistent result at rate O (√m2 log d)/n) to the target distance matrix with at most m nonzeros per row. Moreover, from the implementation perspective, this l1-penalized log-determinant formulation can be efficiently optimized in a block coordinate descent fashion which is much faster than the standard semi-definite programming which has been widely adopted in many other advanced distance learning algorithms. We compare this algorithm with other state-of-the-art ones on various datasets and competitive results are obtained.